Synthetic medical data generation: state of the art and application to trauma mechanism classification

TL;DR

This paper reviews current machine learning techniques for creating synthetic medical data, emphasizing their use in trauma mechanism classification, and proposes a method for generating high-quality combined tabular and textual synthetic records.

Contribution

It introduces a novel methodology for generating high-quality synthetic medical records that integrate tabular and unstructured text data for trauma classification.

Findings

Review of state-of-the-art data generation methods

Proposed method for high-quality synthetic medical records

Application to trauma mechanism classification

Abstract

Faced with the challenges of patient confidentiality and scientific reproducibility, research on machine learning for health is turning towards the conception of synthetic medical databases. This article presents a brief overview of state-of-the-art machine learning methods for generating synthetic tabular and textual data, focusing their application to the automatic classification of trauma mechanisms, followed by our proposed methodology for generating high-quality, synthetic medical records combining tabular and unstructured text data.